Bangladesh BD: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data was reported at 35.200 % in 2010. This records a decrease from the previous number of 43.800 % for 2005. Bangladesh BD: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data is updated yearly, averaging 43.800 % from Dec 2000 (Median) to 2010, with 3 observations. The data reached an all-time high of 52.300 % in 2000 and a record low of 35.200 % in 2010. Bangladesh BD: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Poverty and Inequality. Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

BD: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 18.700 % in 2022. BD: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 18.700 % from Dec 2022 (Median) to 2022, with 1 observations. The data reached an all-time high of 18.700 % in 2022 and a record low of 18.700 % in 2022. BD: Poverty Headcount Ratio at National Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Poverty and Inequality. National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year.;World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.;;This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

For countries with an active poverty monitoring program, the World Bank—in collaboration with national institutions, other development agencies, and civil society—regularly conducts analytical work to assess the extent and causes of poverty and inequality, examine the impact of growth and public policy, and review household survey data and measurement methods. Data here includes poverty and inequality measures generated from analytical reports, from national poverty monitoring programs, and from the World Bank’s Development Research Group which has been producing internationally comparable and global poverty estimates and lines since 1990.

Poverty ratio at $3.2 a day of Bangladesh dropped by 12.83% from 60.0 % in 2010 to 52.3 % in 2016. Since the 0.97% upward trend in 1991, poverty ratio at $3.2 a day plummeted by 37.37% in 2016. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Abstract

The main objective of the Bangladesh Poverty and Groundwater Salinity Survey (BPGSS) 2016 is to understand the linkages between groundwater salinity and poverty in coastal areas in Bangladesh. It is also to assess the extent to which high water salinity might be associated with poor health outcomes among women and children, and identify potential coping and adaptation mechanisms, which households might be using to address high water salinity in these areas.

Geographic coverage

Regional coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE The Bangladesh Poverty and Groundwater Salinity Survey 2016 collected data from a total of 1,500 households in three sub-districts or upazilas in Bangladesh - 500 households in each upazila distributed across 50 primary sampling units (PSUs). The three upazilas selected for this study are the following: (i) Taltoli upazila in the Barguna district of the Barisal division; (ii) Morrelganj upazila in the Bagerhat district of the Khulna division; and (iii) Shyamnagar upazila in the Satkhira district in the Khulna division. Each upazila was allocated an equal size of households in order to get poverty estimates of similar precision. The sampling frame consists of a list of all rural villages developed by the Bangladesh Bureau of Statistics (BBS) based on the Census Enumeration Areas (CEAs) constructed for the 2011 Census of Population and Housing. PSUs are constructed by dividing rural villages into listing blocks or Enumeration Areas (EAs) of around 50 households each and then randomly selecting one block for listing.

The three upazilas included in this study where selected based on discussion with a water salinity expert in Bangladesh and practical considerations using a two-stage procedure. In the first stage, we combined upazila level poverty data from the official 2010 Bangladesh Poverty Maps with upazila level information on groundwater salinity collected by the Bangladesh Water Development Board (BWDB) with support from the Institute of Water Modelling (IWM). Using these combined dataset, we classified all 146 upazilas in coastal areas in four groups: (i) high water salinity and high poverty rate; (ii) high water salinity and low poverty rates; (iii) low water salinity and high poverty rate; (iv) low water salinity and low poverty rates. Figure 1 shows the spatial distribution of coastal area upazilas based on these four categories. In the second stage, we selected one upazila from each of the first three categories as focal areas for this study after discussion with a groundwater expert on availability of other water-supply options (e.g. managed aquifer recharge) and practical considerations. This categorization of upazilas also serve as our three sampling strata - high water salinity and high poverty rate, high water salinity and low poverty rates, and low water salinity and high poverty rate.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire is available in Bengali and English under the Related Materials tab.

Cleaning operations

Data entry and editing was done by Survey CTO.

The index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. Critically the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS) and Multi-Indicator Cluster Surveys (MICS) The resources subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023)

Bangladesh BD: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 84.200 % in 2016. This records a decrease from the previous number of 87.600 % for 2010. Bangladesh BD: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 92.600 % from Dec 1983 (Median) to 2016, with 9 observations. The data reached an all-time high of 97.100 % in 1983 and a record low of 84.200 % in 2016. Bangladesh BD: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from around 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

2,6 (%) in 2016. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

35.2 (%) in 2010. Rural poverty rate is the percentage of the rural population living below the national rural poverty line.

Abstract

The 2018 Dhaka Low Income Area Gender, Inclusion, and Poverty (DIGNITY) survey attempts to fill in the data and knowledge gaps on women's economic empowerment in urban areas, specifically the factors that constrain women in slums and low-income neighborhoods from engaging in the labor market and supplying their labor to wage earning or self-employment. While an array of national-level datasets has collected a wide spectrum of information, they rarely comprise all of the information needed to study the drivers of Female Labor Force Participation (FLFP). This data gap is being filled by the primary data collection of the specialized DIGNITY survey; it is representative of poor urban areas and is specifically designed to address these limitations. The DIGNITY survey collected information from 1,300 urban households living in poor areas of Dhaka in 2018 on a range of issues that affect FLFP as identified through the literature. These range from household composition and demographic characteristics to socioeconomic characteristics such as detailed employment history and income (including locational data and travel details); and from technical and educational attributes to issues of time use, migration history, and attitudes and perceptions.

The DIGNITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh. It has two main modules: the traditional household module (in which the head of household is interviewed on basic information about the household); and the individual module, in which two respondents from each household are interviewed individually. In the second module, two persons - one male and one female from each household, usually the main couple, are selected for the interview. The survey team deployed one male and one female interviewer for each household, so that the gender of the interviewers matched that of the respondents. Collecting economic data directly from a female and male household member, rather than just the head of the household (who tend to be men in most cases), was a key feature of the DIGNITY survey.

Geographic coverage

The DIGNITY survey is representative of low-income areas and slums of the Dhaka City Corporations (North and South, from here on referred to as Dhaka CCs), and an additional low-income site from the Greater Dhaka Statistical Metropolitan Area (SMA).

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure followed a two-stage stratification design. The major features include the following steps (they are discussed in more detail in a copy of the study's report and the sampling document located in "External Resources"):

FIRST STAGE: Selection of the PSUs

Low-income primary sampling units (PSUs) were defined as nonslum census enumeration areas (EAs), in which the small-sample area estimate of the poverty rate is higher than 8 percent (using the 2011 Bangladesh Poverty Map). The sampling frame for these low-income areas in the Dhaka City Corporations (CCs) and Greater Dhaka is based on the population census of 2011. For the Dhaka CCs, all low-income census EAs formed the sampling frame. In the Greater Dhaka area, the frame was formed by all low-income census EAs in specific thanas (i.e. administrative unit in Bangladesh) where World Bank project were located.

Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio between 8 and 10 percent; the second stratum between 11 and 14 percent; and the third stratum, those exceeding 15 percent.

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Only slums in the Dhaka City Corporations are included. Again, three strata were used to sample the slums. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, 100 or more households. Small slums with fewer than 50 households were not included in the sampling frame. Very small slums were included in the low-income neighborhood selection if they are in a low-income area.

Altogether, the DIGNITY survey collected data from 67 PSUs.

SECOND STAGE: Selection of the Households

In each sampled PSU a complete listing of households was done to form the frame for the second stage of sampling: the selection of households. When the number of households in a PSU was very large, smaller sections of the neighborhood were identified, and one section was randomly selected to be listed. The listing data collected information on the demographics of the household to determine whether a household fell into one of the three categories that were used to stratify the household sample:

i) households with both working-age male and female members; ii) households with only a working-age female; iii) households with only a working-age male.

Households were selected from each stratum with the predetermined ratio of 16:3:1. In some cases there were not enough households in categories (ii) and (iii) to stick to this ratio; in this case all of the households in the category were sampled, and additional households were selected from the first category to bring the total number of households sampled in each PSU to 20.

The total sample consisted of 1,300 households (2,378 individuals).

Sampling deviation

The sampling for 1300 households was planned after the listing exercise. During the field work, about 115 households (8.8 percent) could not be interviewed due to household refusal or absence. These households were replaced with reserved households in the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the survey were developed by the World Bank, with assistance from the survey firm, DATA. Comments were incorporated following the pilot tests and practice session/pretest.

Cleaning operations

Collected data was entered into a computer by using the customized MS Access data input software developed by Data Analysis and Technical Assistance (DATA). Once data entry was completed, two different techniques were employed to check consistency and validity of data as follows:

1. Five (5%) percent of the filled-in questionnaire was checked against entered data to measure the transmission error or typos, and;
2. A logical consistency checking technique was employed to identify inconsistencies using SPSS and or STATA software.

Bangladesh BD: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data was reported at 21.300 % in 2010. This records a decrease from the previous number of 28.400 % for 2005. Bangladesh BD: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data is updated yearly, averaging 28.400 % from Dec 2000 (Median) to 2010, with 3 observations. The data reached an all-time high of 35.200 % in 2000 and a record low of 21.300 % in 2010. Bangladesh BD: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Poverty and Inequality. Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Here we provide poverty data created using Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to geolocated household survey data on poverty from the DHS wealth index (2011), the Progress out of Poverty Index (2014), and household income (2013). Citation: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

The Bangladesh Interactive Poverty Maps allow you to explore and visualize socioeconomic data at the zila (district) and upazila (sub-district) level. The tool provides users an easy way to access different types of indicators including poverty, demographics of the population, children’s health and nutrition, education, employment, and access to energy, water, and sanitation services. These maps were constructed by combining three different data sources all of which are publicly available. These include the 2010 Bangladesh Poverty Maps, the IPUMS sample from the 2011 Bangladesh Census of Population and Housing, and the 2012 Undernutrition Maps of Bangladesh.

Definition of variables and data sources

These maps were constructed by combining three different data sources all of which are publicly available. These include the 2010 Bangladesh Poverty Maps, the 2011 Census of Population and Housing sample available from the Integrated Public Use Microdata Series project (IPUMS), and the 2012 Undernutrition Maps of Bangladesh.

The 2010 Bangladesh Poverty Maps technical report describing the metholody used to construct the zila and upazila national poverty statistics can be accessed at the following link: http://www.worldbank.org/en/news/feature/2014/09/30/poverty-maps

The Population and Housing Census sample (IPUMS) dataset can be accessed at the following link: https://international.ipums.org/international-action/sample_details/country/bd

The undernutrition maps produced by the World Food Program (WFP) are available at the following link: https://www.wfp.org/content/undernutrition-maps-bangladesh-2012

Detailed information describing the construction of the variables and sources is presented below.

Basic information:

1) Total population: Total population in the zila/upazila. 2) Share of rural population: Share of the zila/upazila population who lives in rural areas. 3) Working population: Total number of working age population (15-64 years) in zila/upazila. 4) Total households: Total number of households in the zila/upazila.

Source: Indicators 1, 2, 3, and 4 were computed using the 2011 Census of Population and Housing.

Poverty (among the population):

5) Poverty headcount ratio (%): Percentage of the population that lives below the official national upper poverty line. 6) Extreme poverty headcount ratio (%): Percentage of the population that lives below the official national lower poverty line. 7) Percentage of population in bottom 40%: Percentage of the population in the zila/upazila that belongs to the bottom 40% of the national real per capita consumption distribution.

Source: Indicators 5, 6, and 7 come from 2010 Bangladesh Poverty Maps. The total number of poor, extreme poor, and population that belongs to the bottom 40% were computed using indicators 5, 6, 7 and indicator 1 (Total population in the zila/upazila).

Demographic (among population):

8) Population between 0 and 6 years old: Total population in the age range of 0-6 years old. 9) Population between 7 and 14 years old: Total population in the age range of 7-14 years old. 10) Population between 15 and 64 years old: Total population in the age range of 15-64 years old. 11) Population ages 65 and above: Total population in the age range of 65 and above.

Source: Indicators 8, 9, 10, and 11 were constructed using question 14 from the 2011 Census of Population and Housing.

Question 14. Age (Completed years)

Nutrition (among children below 5):

12) Percentage of underweight children: Percentage of children under five years of age whose standarized weight-for-age is more than two standard deviations below the median for the international reference. population (WHO standard) 13) Percentage of severely underweight children: Percentage of children under five years of age whose standarized weight-for-age is more than three standard deviations below the median for the international reference population (WHO standard). 14) Percentage of stunted children: Percentage of children under five years of age whose standarized height-for-age is more than two standard deviations below the median for the international reference population (WHO standard). 15) Percentage of severely stunted children: Percentage of children under five years of age whose standarized weight-for-age is more than three standard deviations below the median for the international reference population (WHO standard).

Source: Indicators 12, 13, 14, and 15 were produced by the World Food Program (WFP) and are constructed based on data from the Child and Mother Nutrition Survey of Bangladesh 2012 (MICS) and the Health and Morbidity Status Survey 2011 (HMSS). The total number of children under the age of 5 years was estimated using data from the 2011 Census of Population and Housing.

Primary Employment (among working population):

16) Agriculture: If employed, sector of employment is agriculture. 17) Industry: If employed, sector of employment is industry. 18) Services: If employed, sector of employment is services.

Source: Indicators 16, 17, and 18 were constructed using Question 25 from the 2011 Census of Population and Housing. Question 25 was asked for persons 7 years of age and older who reported being employed.

Question 25. If employed, field of employment (1) Agriculture (2) Industry (3) Service

Energy & Sanitation (among households):

19) With Electricity: Percentage of households with access to electricity. 20) With flush toilet: Percentage of households with access to flush toilet. 21) With non-flush, latrine: Percentage of households with access to latrine. 22) Without toilet, open defecation: Percentage of households who practice open defecation. 23) With access to tap water: Percentage of households with access to tap water. 24) With access to tube-well water: Percentage of households with access to tube-well water.

Source: Indicators 19, 20, 21, 22, 23, and 24 were constructed using questions 8, 9 and 10 from the 2011 Census of Population and Housing.

Question 8. Source of drinking water (1) Tap (2) Tube-well (3) Other

Question 9. Toilet facilities (1) Sanitary (with water seal) (2) Sanitary (no water seal) (3) Non-sanitary (4) None

Question 10. Electricity connection (1) Yes (2) No

Literacy & Educational Attainment (among adults 18 years and above)

25) Literate population: Percentage of adults who can write a letter. 26) Less than primary completed: Percentage of adults who have not completed primary education. 27) Primary completed: Percentage of adults who have completed primary education. 28) Secondary completed: Percentage of adults who have completed secondary education. 29) University completed: Percentage of adults who have completed univeristy.

Source: Indicators 25, 26, 27, 28, and 29 were constructed using Questions 21 and 23 from the 2011 Census of Population and Housing.

Question 21. Highest class passed (write class passed code)

Question 23. Can write a letter? (1) Yes (2) No

School attendance (among school-age children)

30) Overall (6-18 year olds): Percentage of children 6-18 years old who attend school. 31) Primary level (6-10 years): Percentage of children 6-10 years old who attend school. 32) Junior level (11-13 years): Percentage of children 11-13 years old who attend school. 33) Secondary level (14-15 years): Percentage of children 14-15 years old who attend school. 34) High secondary level (16-18 years): Percentage of children 16-18 years old who attend school.

Source: Indicators 30, 31, 32, 33, and 34 were constructed using Question 20 from the 2011 Census of Population and Housing.

Question 20. Student (Currently) (1) Yes (2) No

Additional Notes: * All national averages reported correspond to weighted upazila/zila level means, except for the nutrition variables and the population in national bottom 40% which correspond to unweighted upazila/zila level means.

Abstract

Under World Bank contracts (two separate contracts), the Data Analysis and Technical Assistance (DATA) have conducted a three-round survey “Addressing Extreme Poverty in Bangladesh: The Case of monga” in the Northwestern region of Bangladesh to have a better understanding of the causes of the occurrence and persistence of Monga. This survey is specifically designed to permit a scientific and rigorous assessment, of impacts of the Monga which would have implications for policy to mitigate Monga, through follow-up surveys.

The South Asia PREM Sector Unit (SASPR) of the World Bank have undertaken this aforesaid survey. This region experiences seasonal deprivation and a famine-like situation, locally known as Monga, with alarming regularity, along with high incidence of chronic poverty. The primary objective of the task is to provide a better understanding of the causes of the occurrence and persistence of Monga, which would also have implications for broader understanding of extreme poverty in Bangladesh; implications for policy, including proposals for pilot interventions, to help relevant actors such as the government, the NGOs, and the donors to mitigate Monga. The funds for this work have come from the UK Department for International Development (DFID) trust fund (TF091124).

Geographic coverage

The survey covered the following ares, Monga-prone greater Rangpur districts (Kurigram, Gaibandha, Lalmonirhat, Nilphamari, Rangpur) and Bogra district, in the northwestern.

Analysis unit

• Households
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey Areas and Survey Population

The survey has both a household level and a community level questionnaire. The survey has households belonging to the Monga-prone greater Rangpur districts (Kurigram, Gaibandha, Lalmonirhat, Nilphamari, Rangpur) with the sampling frame being representative of the diversity of the region. For example, the survey covered the various agro-ecological zones of the region, include households on river banks and away from it, include mainland households as well as char-land households. In addition to Monga area households, the survey has sample from non-Monga area households in the northwestern Bogra district. It is determined by the World Bank that the total sample size is 2,375 rural households selected from about 125 villages/PSUs. There are 95 PSUs in 5 districts of greater Rangpur (Lalmonirhat, Kurigram, Nilphamari, Gaibandha and Rangpur) and 30 PSUs in Bogra districts.

Survey Design and Sampling Strategy

It was decided by the World Bank that the sample size for the study is 2,375 households. Out of these 2,375 households, 1,805 households from 95 PSUs/villages were from Monga-prone greater Rangpur districts (Kurigram, Gaibandha, Lalmonirhat, Nilphamari, Rangpur) and rest 570 households were from non-Monga area, Bogra district, in the northwestern. Sample list of the villages/PSUs from each of the districts were provided by the World Bank.

In the first round of survey, it entailed two sub-surveys: a) Household listing operation: census of households in selected 125 villages with average size of 250 households, if a village size was more than 250 households, village was split using natural/constructed demarcation line such as by using river/canal or village road; and b) the detailed household survey and community component. For the later one it required on-site sampling of required number of households using random sampling technique from the census list and then conducted the household survey on sampled households. For the household sampling from census list a simple random sample selection protocol was developed and used for on-site sample selection. For follow-up 2 rounds of surveys same sample households were tracked and surveyed, only exception is there was no census hence no on-site sampling procedure was adopted for later rounds.

Sampling Protocol

Since village census was compulsory to list all household in the PSUs to sample required 19 households for detailed household survey, it was decided to do the census on the first day of survey in each of the PSUs and in the evening once the census is completed an on-site sampling technique was used to sample 19 households per PSU.

Below is a practical example on sample selection procedure adopted in field after the census is completed. Simple Random Selection of households was performed as follows:

a) Total number of household in the sample village/PSU; in this case it was 300. b) Total number of sample to be selected. In this case the sample size is 19 per PSU. c) So an "Interval" (steps) is estimate by dividing: Total Population/Sample size. In this case 300/19 = 15.79 d) A random number was generated for each of the PSU by using excel worksheet function "rand()". In this case it was 0.164 e) Then the "Interval (step)" was multiplied by random number "Interval X Random number", it gave the value for first sampling number (a non-integer number) = 2.594689447.

Now for the second sampling number the "Interval" value added with first sampling number, for third sampling number again the "Interval value" with second sampling number, and so on until we get all 19 sampling number.

The census was listed per PSU from 1 to Nth number as long as it required to number all the listed households in a PSU. As the sampling numbers are non-integer number, to get an integer that corresponds to the listing number of all households the sampling numbers were converted into integer and added "1" to get the household sample number.

Mode of data collection

Face-to-face [f2f]

Research instrument

Development of Questionnaires

The draft questionnaires for the study have been provided by the World Bank. Later Refinement and adaptation of the questionnaires has been done. Revisions of the questionnaires took place mainly at two instances: 1) initial revision by Consultant upon receipt of the questionnaires; and 2) following the practice session/pretest (testing stage), where the need for further revisions and adaptation has transpired.

After the first draft of questionnaires are received from the World Bank the questionnaires have been revised, modified and reformatted to make it more survey friendly. Then a formative research trip to North-western region (mainly Rangpur and Kurigram) of Bangladesh was arranged to understand living condition and how the people in the monga prone area response in crises. Dr. Umar Serajuddin from the World Bank and Md. Zobair from DATA participated in the formative research trip. After the formative research trip, first draft of the questionnaire has further been revised according to the understanding from formative research.

In follow-up rounds modification of questionnaires mainly due to addition of some questions to or deletion of modules from the survey instrument already fielded in the first round of the survey.

The Census questionnaire collected general information such as, household location, religion, electrification, housing characteristics, land owned by the household, ever migrated for work, participation in 100 days EGP program membership in MFI, asset listing. Furthermore the census survey collected demographic information of the household that includes member specific sex, age, marital status literacy, education in terms of highest class passed, attending school, main occupation. Census questionnaire has been administered to each of the household in the selected villages. For a village the survey considered maximum of 250 household as village size; if a village size was more than 250 households, village has been split using natural/constructed demarcation line such as river/canal or village road. Once the census has been completed, 19 households from each of the villages sampled using random sampling technique for detailed household survey.

Household questionnaire has been administered to each household selected for the survey. The household head, main respondent, was interviewed to complete the household questionnaire. In specific module there are multiple respondent. For an example while interviewing food consumption module, the quantity consumed is answered by the main female while the price of the item was answered by the main male member of the household.

Community questionnaire has been administered to various community leaders such as the large farmer, the principal of a school, local elites. A community questionnaire has been completed in each village in which a household is selected for the survey.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Climate Change, External Debt, Trade.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Bangladesh BD: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 14.000 % in 2022. This records an increase from the previous number of 6.100 % for 2016. Bangladesh BD: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 5.250 % from Dec 1983 (Median) to 2022, with 10 observations. The data reached an all-time high of 14.000 % in 2022 and a record low of 3.600 % in 1985. Bangladesh BD: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Bangladesh BD: Poverty Gap at National Poverty Lines: % data was reported at 6.500 % in 2010. This records a decrease from the previous number of 9.000 % for 2005. Bangladesh BD: Poverty Gap at National Poverty Lines: % data is updated yearly, averaging 9.000 % from Dec 2000 (Median) to 2010, with 3 observations. The data reached an all-time high of 12.800 % in 2000 and a record low of 6.500 % in 2010. Bangladesh BD: Poverty Gap at National Poverty Lines: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Poverty and Inequality. Poverty gap at national poverty lines is the mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

In this analysis we have combined several data sources around the floods in Bangladesh in August 2017.

Visualization

• See attached map for a map visualization of this analysis.
• See http://bit.ly/2uFezkY for a more interactive visualization in Carto.

Situation

Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action.

Indicators and sources

In the attached map, we combined several sources:

• Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20)
• Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.)
• Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis.

Detailed methodology Vulnerability

• The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop).
• The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas.
• Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).

Bangladesh BD: Poverty Gap at National Poverty Lines: Urban: % data was reported at 4.300 % in 2010. This records a decrease from the previous number of 6.500 % for 2005. Bangladesh BD: Poverty Gap at National Poverty Lines: Urban: % data is updated yearly, averaging 6.500 % from Dec 2000 (Median) to 2010, with 3 observations. The data reached an all-time high of 9.100 % in 2000 and a record low of 4.300 % in 2010. Bangladesh BD: Poverty Gap at National Poverty Lines: Urban: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Poverty and Inequality. Urban poverty gap at national poverty lines is the urban population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.